Renewable energy siting and performance
The resource map decides the business case before the first panel ships.
Twenty years of cloud data beats six months of a pyranometer.Solar irradiance climatology and site screening
Geostationary satellite retrievals of GHI and DNI give solar developers a statistically stable irradiance climatology before a single ground instrument is deployed. The method works, but dusty skies and bright desert surfaces introduce biases that must be corrected explicitly.
SEVIRI on Meteosat-9/10/11 (EUMETSAT)ABI on GOES-16/18 (NOAA)MODIS Terra/Aqua (NASA)
A mast measures one point. SAR measures the whole sea.Near-surface wind-resource mapping from SAR
C-band SAR backscatter, inverted through geophysical model functions, yields 10 m wind-speed maps at 100–500 m spatial detail across open water. A single overpass is a snapshot, not a climatology, so statistical fusion with ERA5 or MERRA-2 reanalysis is essential before any investment decision.
Sentinel-1A/B SAR-C (ESA/Copernicus)RADARSAT-2 (MDA/CSA)ALOS-2 PALSAR-2 (JAXA)
Satellites narrow the search area. Buoys still close the deal.Offshore wind site screening using multi-parameter satellite data
Combining SAR wind fields, radar-altimeter wave climatology, sea-ice extent, and AIS vessel density lets developers shortlist offshore lease areas before committing to costly met-ocean campaigns. Satellite data accelerates the funnel; it does not replace in-situ measurement for bankable energy yield.
Sentinel-1 SAR-C (ESA)CryoSat-2 radar altimeter (ESA)Sentinel-6 Michael Freilich altimeter (ESA/EUMETSAT/NASA)
Rain gauges lie. Mountains make it worse.Hydro-resource assessment from satellite precipitation climatology
Multi-satellite merged precipitation products can estimate long-term runoff potential in basins where ground gauges are absent or unreliable, but orographic bias and coarse resolution demand careful uncertainty accounting before any investment decision.
GPM IMERG (GMI + DPR)TRMM TMPA (legacy)PERSIANN-CDR
Snow depth saturates the microwave signal. The melt keeps going.Snowpack water-equivalent monitoring for hydro-power forecasting
Passive microwave and optical sensors together estimate the water stored in mountain snowpacks weeks before it reaches a turbine. Neither sensor alone is sufficient, and honest fusion of both is what makes operational hydro forecasting work.
AMSR2 on JAXA GCOM-W1MODIS Terra and Aqua (MOD10A1 / MYD10A1)VIIRS I-band snow products (VNP10A1)
Concrete is poured. Panels go down. Satellites keep score.Solar farm construction progress monitoring from satellite imagery
High-resolution optical and SAR time series let lenders and EPC managers verify ground-clearing, racking, and panel placement at utility-scale solar sites independently of contractor reporting. Change-detection on Sentinel-2 and sub-metre commercial imagery resolves individual panel rows at sites above roughly 50 MW.
Sentinel-2 MSIPlanet SuperDoveMaxar WorldView-3
Your SCADA says one thing. The sky says another.Operational solar-farm performance anomaly detection
Satellite-derived irradiance from SEVIRI and Sentinel-2 can be compared against SCADA generation data to flag underperforming sub-arrays, isolating soiling, shading, and inverter faults as distinct spatial signatures without a site visit.
SEVIRI on Meteosat Second GenerationSentinel-2 MSILandsat 8/9 OLI+TIRS
Dust settles invisibly. Satellite aerosol columns make it legible.Panel soiling and dust-deposition detection from satellite spectral indices
Aerosol optical depth retrievals from MODIS MAIAC and TROPOMI, combined with MERRA-2 dust reanalysis, let operators estimate soiling rates on PV panels and schedule cleaning before yield losses compound.
MODIS MAIAC AOD (Terra/Aqua)Sentinel-5P TROPOMI aerosol indexMERRA-2 dust reanalysis
Panels fade slowly. A five-year archive notices before your inverters do.Long-term panel degradation tracking via satellite reflectance time series
Encapsulant browning, delamination, and potential-induced degradation all shift a panel's optical signature over years. Dense Landsat and Sentinel-2 archives, properly normalised, can detect those shifts at the farm scale before they appear in financial audits.
Landsat 8/9 OLISentinel-2 MSIMODIS MCD43A BRDF/Albedo product
Turbines slow the wind for kilometres. SAR makes that visible.Wind-farm wake mapping from SAR wind-speed retrievals
Synthetic aperture radar retrieves 10-metre wind-speed fields precise enough to resolve velocity deficits and turbulence signatures in the lee of operating offshore wind farms, giving developers and operators quantitative wake data without deploying a single met mast.
Sentinel-1A/B SAR-C (IW mode)Sentinel-1 EW modeRADARSAT-2 Fine-Beam Single (FBS)
The cheapest route on paper often crosses the costliest ground in practice.Grid-connection route planning using satellite-derived land-cover and terrain
Satellite land-cover and elevation data can cut months from grid-connection corridor studies, but classification errors in fragmented farmland and the irreducible need for wayleave negotiation mean satellite outputs are a starting point, not a final answer.
Sentinel-2 MSI (ESA WorldCover)Copernicus DEM GLO-30SRTM 30 m
Four physical constraints. One candidate list. Most sites fail two of them.Green-hydrogen site screening via combined solar, wind, and water-availability data
Electrolytic hydrogen needs sun or wind, water, and a route to market, all in the same place. Satellite screening combines GHI climatology, SAR wind proxies, GRACE groundwater anomalies, and JRC surface-water layers to cut a continental candidate list to a workable shortlist before a single survey crew is deployed.
SEVIRI (Meteosat) / ABI (GOES)Sentinel-1 SAR-C (ESA)GRACE-FO (NASA/DLR)
A reservoir's surface lies. Its volume is harder to fake.Hydro-reservoir water-level and storage-volume monitoring
Radar altimeters and imaging spectrometers can track water-surface elevation and inundated area independently, then combine them into storage-volume estimates that dam operators rarely publish themselves. Accuracy hinges on bathymetry you almost certainly do not have.
Sentinel-6 Michael Freilich (Poseidon-4 radar altimeter)SWOT KaRIn (Ka-band Radar Interferometer)ICESat-2 ATLAS (photon-counting lidar)
Turbines need current speed at hub height. Satellites see the surface.Tidal-stream current-resource mapping from satellite altimetry and SAR
Satellite altimetry and SAR imagery can screen coastal straits for tidal-stream energy potential before a survey vessel is chartered, but they cannot replace in-water profiling for turbine design.
Sentinel-1 SAR-C (ESA)Jason-3 radar altimeter (CNES/EUMETSAT/NOAA/NASA)FES2014 tidal model (LEGOS/CNES, altimeter-assimilated)
Decades of wave data, one narrow line across the ocean at a time.Wave-energy resource climatology from satellite altimetry
Radar altimeters on Sentinel-6, Jason-3, and SARAL/AltiKa have measured significant wave height globally since the early 1990s. Combined with ERA5 reanalysis, these records yield the exceedance statistics and wave-power climatologies that wave-energy converter projects need before a single buoy is deployed.
Sentinel-6 Michael FreilichJason-3SARAL/AltiKa
Water bodies look permanent on a map. Seasonally, many are not.Inland water-body screening for floating solar deployment
Satellite imagery can rank hundreds of reservoirs, irrigation ponds, and mine-water lakes for floating PV suitability in weeks. The analysis combines surface-area mapping, seasonal water-level change, and topographic shading to filter out sites that will not pencil out economically.
Sentinel-2 MSILandsat 8/9 OLISRTM DEM
Panels shade the crop. Satellites still read the stress.Agrivoltaic system crop-stress co-monitoring
Bifacial solar arrays create spatially heterogeneous shading that ground sensors miss entirely. Satellite-derived vegetation indices from Sentinel-2 red-edge bands and Planet SuperDove resolve inter-row and under-panel zones separately, but only after correcting for the spectral contamination that panel shadows introduce into surface-reflectance retrievals.
Sentinel-2 MSIPlanet SuperDoveLandsat 8/9 OLI
Dust and smog cut your modelled yield. Satellites can measure how much.Aerosol optical depth correction for solar irradiance estimation
Uncorrected satellite irradiance products overestimate solar yield in dust-prone and polluted regions. AOD retrievals from MODIS, VIIRS, and Sentinel-5P TROPOMI quantify column aerosol loading so that irradiance estimates reflect what actually reaches a panel.
MODIS Terra/Aqua (MOD04/MYD04)VIIRS (AERDB/AERDT products)Sentinel-5P TROPOMI
Ice does not announce itself. Backscatter does.Wind-turbine blade icing detection from passive microwave and optical data
Blade icing is a leading cause of unplanned wind-farm downtime in cold climates. Satellite SAR and passive microwave data can flag icing conditions before production losses compound, though resolution limits mean site-level interpretation requires careful method layering.
Sentinel-1 SAR (C-band, 5.4 GHz)AMSR2 passive microwave (JAXA GCOM-W)MODIS Terra/Aqua Land Surface Temperature
Scour hides underwater. The surface current it creates does not.Offshore wind foundation scour monitoring from satellite SAR
Seabed scour around monopile foundations threatens structural integrity, yet routine sonar surveys are expensive and infrequent. Satellite SAR detects the surface roughness anomalies that accelerated tidal flow around a scoured foundation produces, providing an early-warning flag between in-situ inspection campaigns.
Sentinel-1 SAR (C-band, 5.405 GHz)TerraSAR-X / TanDEM-X (X-band, 9.65 GHz)Sentinel-2 MSI (optical, 10 m visible bands)
Desert turns dark. The microclimate notices before the grid does.Land-surface albedo change from large-scale solar farm deployment
Utility-scale solar farms replace high-albedo desert and grassland with lower-albedo panel arrays. MODIS MCD43, Sentinel-3 OLCI, and Landsat OLI can quantify that shift, with direct implications for microclimate modelling and energy-yield validation.
MODIS MCD43A3 (Terra/Aqua)VIIRS VNP43MA3 (Suomi-NPP / NOAA-20)Sentinel-3 OLCI
Turbines attract lightning. Satellites tell you how often.Lightning strike climatology for offshore wind risk assessment
Spaceborne lightning sensors on TRMM, the ISS, and GOES geostationary platforms map flash density at 0.5-degree resolution across decades, giving wind developers and insurers a quantified strike-risk baseline before a single foundation is poured.
TRMM Lightning Imaging Sensor (LIS)ISS Lightning Imaging Sensor (ISS-LIS)GOES-16/17 Geostationary Lightning Mapper (GLM)
ERA5 assumes a field. Your site is not a field.Terrain roughness and surface roughness length mapping for wind-farm micrositing
Aerodynamic roughness length (z0) derived from satellite land-cover and canopy-height data replaces coarse reanalysis assumptions in wind-flow models, improving annual energy production estimates at the micrositing stage. This page explains the sensors, methods, and honest limits of the approach.
TanDEM-X (DLR)Sentinel-2 MSI (ESA/Copernicus)GEDI (NASA/GSFC)
Clouds move on physics. Grids move on predictions.Cloud-shadow nowcasting for solar-farm output ramp prediction
High-cadence geostationary imagery from MSG SEVIRI, GOES-16 ABI and Himawari-9 AHI can track cloud shadows across solar farms every 5–15 minutes, giving grid operators a short warning window before output ramp events. Accuracy is honest: useful to roughly 30 minutes ahead, degrading sharply beyond that.
MSG SEVIRI (Meteosat Second Generation)GOES-16 ABI (Advanced Baseline Imager)Himawari-9 AHI (Advanced Himawari Imager)
Ice retreats on its own schedule. Turbine foundations cannot.Sea-ice seasonal extent mapping for offshore wind operability windows
Passive microwave and SAR imagery together define when Arctic offshore wind sites are operable and what ice loads the structures must survive. Neither sensor alone is sufficient.
Sentinel-1 SAR (C-band, ESA)AMSR2 (JAXA, aboard GCOM-W)RADARSAT-2 (C-band SAR, MDA)
Satellites measure canopy, not calories. Here is what that distinction costs you.Biomass energy feedstock density mapping from satellite vegetation data
SAR backscatter and optical vegetation indices can estimate above-ground biomass density across large areas, but saturation limits, cloud cover, and the gap between total biomass and harvestable residue all constrain what the data can honestly deliver.
Sentinel-1 SAR (C-band, ESA)ALOS-2 PALSAR-2 (L-band, JAXA)GEDI (Global Ecosystem Dynamics Investigation, NASA/UMD)
Flat-terrain yield models lie. Mountain valleys pay the difference.Terrain horizon shading and self-shading loss mapping for solar sites
In complex topography, standard irradiance models routinely overestimate annual solar yield by several percent. Satellite-derived DEMs and ray-casting horizon analysis quantify exactly how much terrain is stealing your sunlight.
TanDEM-X DEMCopernicus GLO-30 DEMALOS World 3D AW3D30
Sonar costs millions. Satellites can tell you where not to look first.Offshore export cable route seabed characterisation from satellite-derived bathymetry
Multispectral satellite imagery can resolve shallow-water bathymetry and broad seabed sediment classes down to roughly 25 m in clear water, giving cable-route planners a low-cost corridor filter before committing to geophysical survey.
Sentinel-2 MSIWorldView-3Planet SuperDove
SAR sees the sea surface. Hub height is 150 metres up.Wind-speed vertical profile estimation from SAR and spaceborne lidar fusion
Satellite SAR retrieves wind speed at 10 m above the surface with high spatial resolution, but wind turbines operate at 100–200 m. Bridging that gap requires atmospheric stability correction and spaceborne wind-profile data, and the method is less straightforward than it looks.
Sentinel-1 SAR (C-band, 5.4 GHz)Aeolus ALADIN Doppler wind lidarASCAT on MetOp (C-band scatterometer)
Thousands of metres of head difference, visible from orbit before a boot touches ground.Pumped-hydro energy storage site screening from satellite topography
Satellite-derived digital elevation models can screen entire continents for paired pumped-hydro reservoir sites in days, ranking candidates by potential energy storage capacity before any field investigation begins. The method is fast and cheap; it is also blind to geology, land tenure, and ecology.
TanDEM-X DEMCopernicus GLO-30 DEMSRTM
Grass grows quietly. Shading losses and fire risk do not.Vegetation encroachment monitoring at operational solar farms
Unmanaged vegetation beneath and around solar arrays shades panels and raises fire risk, yet satellite detection requires careful sensor selection: Sentinel-2's 10 m pixels miss sub-panel patches that Planet's 3–4 m imagery resolves.
Sentinel-2 MSIPlanet SuperDoveLandsat 8/9 OLI
Ships turn off their transponders. They cannot turn off their radar cross-section.Vessel traffic conflict mapping around offshore wind development zones
Integrating satellite AIS with Sentinel-1 SAR reveals the full picture of vessel traffic inside proposed offshore wind lease areas, including dark vessels that never appear in transponder records. The analysis directly supports maritime spatial planning and cable-route risk assessment.
Sentinel-1 SAR (C-band, ESA)Satellite AIS (multiple commercial providers, e.g. Spire Global)Sentinel-2 MSI (ESA)
Boreholes cost millions. Thermal pixels cost almost nothing first.Geothermal resource surface heat anomaly mapping for power siting
Thermal infrared satellites can flag surface heat anomalies linked to geothermal activity before a single borehole is drilled. This page explains what Landsat 8/9 TIRS, ASTER, and ECOSTRESS actually resolve, and where the physics stops.
Landsat 8/9 TIRSASTER TIRECOSTRESS
Panels generate power and heat. The heat has consequences.Solar farm surface heat-island effect and microclimate characterisation
Large photovoltaic arrays suppress latent heat flux, raising land-surface temperature by measurable margins detectable in Landsat TIRS and ECOSTRESS imagery. The thermal footprint matters for environmental permitting and for module-efficiency accounting.
Landsat 8/9 TIRSECOSTRESS (ISS-mounted)Sentinel-2 MSI
The wind at 10 m is not the wind at 150 m. Stability explains the gap.Atmospheric stability classification for offshore wind shear and turbulence assessment
Marine atmospheric boundary layer stability controls how wind speed and turbulence intensity change with height. Combining satellite SST, ERA5 reanalysis, and SAR surface winds lets developers stratify candidate offshore zones by Monin-Obukhov regime before committing to met-mast positions.
Sentinel-1 SAR (C-band, ESA)Meteosat SEVIRI (EUMETSAT)GOES-R ABI (NOAA)
Topographic screening misses floods. A decade of radar does not.Flood inundation frequency mapping for renewable energy site risk screening
Standard elevation models cannot tell a financier how often a solar farm sits under water. Multi-year Sentinel-1 SAR time series map historical inundation extent and frequency at 10 m, cloud-free, and fused with TanDEM-X terrain data to estimate return-period depths.
Sentinel-1 A/B SAR (C-band, 5.405 GHz)TanDEM-X Digital Elevation ModelSRTM (Shuttle Radar Topography Mission)
Point weather stations miss the valley next door. Satellites do not.Regional wind-farm icing-event production-loss mapping from satellite climatology
Blade icing is the largest single cause of cold-climate wind-farm curtailment, yet its spatial pattern is highly localised. Combining MODIS/VIIRS cloud-phase data with ERA5 wet-bulb fields produces grid-scale icing-climate indices that point records cannot.
MODIS Terra / AquaVIIRS NOAA-20 (and Suomi-NPP)ERA5 reanalysis (ECMWF)
Mean irradiance sells projects. Tail-risk irradiance kills them.Interannual solar resource variability linked to ENSO and climate indices
Satellite climatologies from CERES and CM SAF SARAH-3 reveal how ENSO and the Indian Ocean Dipole drive year-to-year swings in solar irradiance across tropical and subtropical regions, letting project financiers build defensible P90 exceedance curves rather than guessing at tail risk.
CERES SYN1degCM SAF SARAH-3MODIS (Terra and Aqua)
Planning rules are fixed. The land cover beneath them is not.Onshore wind development constraint mapping from satellite land-cover classification
Satellite land-cover classification and digital elevation data can pre-screen onshore wind sites against setback, habitat and visual-impact constraints before a single site visit, cutting desktop feasibility time significantly. This page explains the sensors, methods, honest limits and analytic outputs involved.
Sentinel-2 MSIESA WorldCover 10 mCopernicus DEM GLO-30
The wind at hub height is not the wind at the surface.Low-level jet climatology for offshore wind energy density assessment
Low-level jets concentrate kinetic energy at 100–500 m, well above what met-mast networks capture. ERA5 reanalysis combined with Aeolus spaceborne Doppler lidar profiles lets developers quantify jet frequency, seasonal timing, and speed excess before a single anemometer is deployed.
ESA Aeolus (ALADIN DWL)ERA5 reanalysis (ECMWF)MERRA-2 reanalysis (NASA GMAO)
Panels reflect sunlight. Approach paths run straight into it.Solar farm specular glint and aviation glare hazard mapping from satellite geometry
Specular glint from photovoltaic arrays is a regulated aviation hazard in several jurisdictions. Satellite geometry, open DEMs and multispectral reflectance data can screen candidate layouts rapidly before detailed ray-tracing is commissioned.
Sentinel-2 MSILandsat 8/9 OLICopernicus DEM GLO-30
Sediment fills reservoirs quietly. Satellites watch it happen in colour.Reservoir sediment turbidity and siltation monitoring for hydropower asset management
Optical satellites retrieve surface turbidity and total suspended matter concentrations across reservoir surfaces at 10–300 m resolution, giving hydropower operators a cost-effective complement to infrequent bathymetric surveys and early warning of post-flood siltation pulses.
Sentinel-2 MSILandsat 8/9 OLIMODIS Aqua/Terra